






'''simple docstring'''





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MaskFormerSwinBackbone
						from transformers.models.maskformer import MaskFormerSwinModel


class  snake_case__      :




					def __init__(  self  :		str				, _A  :		str				, _A  :		Optional[Any]=13				, _A  :		Dict=32				, _A  :		Any=2				, _A  :		List[str]=3				, _A  :		Union[str, Any]=16				, _A  :		Tuple=[1, 2, 1]				, _A  :		Dict=[2, 2, 4]				, _A  :		str=2				, _A  :		Optional[Any]=2.0				, _A  :		List[str]=True				, _A  :		str=0.0				, _A  :		Dict=0.0				, _A  :		Union[str, Any]=0.1				, _A  :		Union[str, Any]="gelu"				, _A  :		str=False				, _A  :		Union[str, Any]=True				, _A  :		List[str]=0.02				, _A  :		Tuple=1e-5				, _A  :		Optional[int]=True				, _A  :		Dict=None				, _A  :		List[str]=True				, _A  :		str=10				, _A  :		List[str]=8				, _A  :		Dict=["stage1", "stage2", "stage3"]				, _A  :		int=[1, 2, 3]				, )    ->							str:
								UpperCAmelCase_      :       Optional[Any]          =       parent
								UpperCAmelCase_      :       List[Any]          =       batch_size
								UpperCAmelCase_      :       List[Any]          =       image_size
								UpperCAmelCase_      :       int          =       patch_size
								UpperCAmelCase_      :       Dict          =       num_channels
								UpperCAmelCase_      :       List[Any]          =       embed_dim
								UpperCAmelCase_      :       List[Any]          =       depths
								UpperCAmelCase_      :       Optional[int]          =       num_heads
								UpperCAmelCase_      :       Optional[int]          =       window_size
								UpperCAmelCase_      :       List[Any]          =       mlp_ratio
								UpperCAmelCase_      :       Tuple          =       qkv_bias
								UpperCAmelCase_      :       List[str]          =       hidden_dropout_prob
								UpperCAmelCase_      :       int          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       Any          =       drop_path_rate
								UpperCAmelCase_      :       Optional[int]          =       hidden_act
								UpperCAmelCase_      :       Tuple          =       use_absolute_embeddings
								UpperCAmelCase_      :       Dict          =       patch_norm
								UpperCAmelCase_      :       List[Any]          =       layer_norm_eps
								UpperCAmelCase_      :       Dict          =       initializer_range
								UpperCAmelCase_      :       Optional[Any]          =       is_training
								UpperCAmelCase_      :       Any          =       scope
								UpperCAmelCase_      :       List[str]          =       use_labels
								UpperCAmelCase_      :       List[str]          =       type_sequence_label_size
								UpperCAmelCase_      :       Dict          =       encoder_stride
								UpperCAmelCase_      :       Union[str, Any]          =       out_features
								UpperCAmelCase_      :       int          =       out_indices




					def 		A      (  self  :		Union[str, Any]      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase_      :       List[Any]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Dict          =       ids_tensor([self.batch_size]				, self.type_sequence_label_size      )

								UpperCAmelCase_      :       List[Any]          =       self.get_config()

								return config, pixel_values, labels




					def 		A      (  self  :		Union[str, Any]      )    ->							Dict:
								return MaskFormerSwinConfig(
								    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, embed_dim=self.embed_dim				, depths=self.depths				, num_heads=self.num_heads				, window_size=self.window_size				, mlp_ratio=self.mlp_ratio				, qkv_bias=self.qkv_bias				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, drop_path_rate=self.drop_path_rate				, hidden_act=self.hidden_act				, use_absolute_embeddings=self.use_absolute_embeddings				, path_norm=self.patch_norm				, layer_norm_eps=self.layer_norm_eps				, initializer_range=self.initializer_range				, encoder_stride=self.encoder_stride				, out_features=self.out_features				, out_indices=self.out_indices				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[str]				, _A  :		int				, _A  :		Optional[int]      )    ->							Optional[int]:
								UpperCAmelCase_      :       Dict          =       MaskFormerSwinModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Any          =       model(_A      )

								UpperCAmelCase_      :       Optional[int]          =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
								UpperCAmelCase_      :       Tuple          =       int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, expected_seq_len, expected_dim)      )




					def 		A      (  self  :		int				, _A  :		int				, _A  :		Any				, _A  :		Tuple      )    ->							str:
								UpperCAmelCase_      :       List[str]          =       MaskFormerSwinBackbone(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Any          =       model(_A      )

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps      )				, len(config.out_features      )      )
								self.parent.assertListEqual(list(result.feature_maps[0].shape      )				, [13, 16, 16, 16]      )

								# verify channels
								self.parent.assertEqual(len(model.channels      )				, len(config.out_features      )      )
								self.parent.assertListEqual(model.channels				, [16, 32, 64]      )

								# verify ValueError
								with self.parent.assertRaises(_A      ):
											UpperCAmelCase_      :       Tuple          =       ['''stem''']
											UpperCAmelCase_      :       str          =       MaskFormerSwinBackbone(config=_A      )





					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       List[Any]          =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(
					    (
					        MaskFormerSwinModel,
					        MaskFormerSwinBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					a_        =					{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
					a_        =					False
					a_        =					False
					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		List[str]      )    ->							List[str]:
								UpperCAmelCase_      :       int          =       MaskFormerSwinModelTester(self      )
								UpperCAmelCase_      :       Dict          =       ConfigTester(self				, config_class=_A				, embed_dim=37      )




					@require_torch_multi_gpu
					@unittest.skip(
					    reason=(
					        '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
					        ''' `nn.DataParallel`'''
					    )      )
					def 		A      (  self  :		List[str]      )    ->							int:
								pass




					def 		A      (  self  :		Optional[Any]      )    ->							List[str]:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




					def 		A      (  self  :		Tuple      )    ->							Tuple:
								return




					def 		A      (  self  :		int      )    ->							int:
								UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		Optional[int]      )    ->							List[str]:
								UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_A      )




					@unittest.skip('''Swin does not use inputs_embeds'''      )
					def 		A      (  self  :		Dict      )    ->							int:
								pass




					@unittest.skip('''Swin does not support feedforward chunking'''      )
					def 		A      (  self  :		int      )    ->							int:
								pass




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       str          =       model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)      )
											UpperCAmelCase_      :       Tuple          =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A				, nn.Linear      )      )




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Optional[Any]          =       model_class(_A      )
											UpperCAmelCase_      :       Dict          =       inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase_      :       List[Any]          =       [*signature.parameters.keys()]

											UpperCAmelCase_      :       Union[str, Any]          =       ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				, _A      )




					@unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
					def 		A      (  self  :		Any      )    ->							List[Any]:
								pass




					@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
					def 		A      (  self  :		int      )    ->							Any:
								pass




					def 		A      (  self  :		Dict				, _A  :		Any				, _A  :		Dict				, _A  :		Optional[Any]				, _A  :		Union[str, Any]      )    ->							str:
								UpperCAmelCase_      :       Optional[Any]          =       model_class(_A      )
								model.to(_A      )
								model.eval()

								with torch.no_grad():
											UpperCAmelCase_      :       List[Any]          =       model(**self._prepare_for_class(_A				, _A      )      )

								UpperCAmelCase_      :       Optional[int]          =       outputs.hidden_states

								UpperCAmelCase_      :       str          =       getattr(
								    self.model_tester				, '''expected_num_hidden_layers'''				, len(self.model_tester.depths      ) + 1      )
								self.assertEqual(len(_A      )				, _A      )

								# Swin has a different seq_length
								UpperCAmelCase_      :       List[str]          =       (
								    config.patch_size
								    if isinstance(config.patch_size				, collections.abc.Iterable      )
								    else (config.patch_size, config.patch_size)
								)

								UpperCAmelCase_      :       List[Any]          =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]      )				, [num_patches, self.model_tester.embed_dim]				, )




					def 		A      (  self  :		Optional[int]      )    ->							List[str]:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase_      :       Optional[Any]          =       (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size				, collections.abc.Iterable      )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       List[Any]          =       True
											self.check_hidden_states_output(_A				, _A				, _A				, _A      )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											UpperCAmelCase_      :       List[Any]          =       True

											self.check_hidden_states_output(_A				, _A				, _A				, _A      )




					def 		A      (  self  :		Optional[Any]      )    ->							int:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Tuple          =       3

								UpperCAmelCase_      :       List[str]          =       (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size				, collections.abc.Iterable      )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								UpperCAmelCase_      :       Any          =       (
								    config.patch_size
								    if isinstance(config.patch_size				, collections.abc.Iterable      )
								    else (config.patch_size, config.patch_size)
								)

								UpperCAmelCase_      :       str          =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								UpperCAmelCase_      :       str          =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       int          =       True
											self.check_hidden_states_output(_A				, _A				, _A				, (padded_height, padded_width)      )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											UpperCAmelCase_      :       int          =       True
											self.check_hidden_states_output(_A				, _A				, _A				, (padded_height, padded_width)      )




					@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								pass




					@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
					def 		A      (  self  :		List[str]      )    ->							Union[str, Any]:
								pass




					@unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
					def 		A      (  self  :		Dict      )    ->							int:
								pass





					def 		A      (  self  :		Optional[int]      )    ->							Any:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       self.model_tester.prepare_config_and_inputs_for_common()

								def set_nan_tensor_to_zero(_A  :		List[Any]      ):
											UpperCAmelCase_      :       str          =       0
											return t

								def check_equivalence(_A  :		Dict				, _A  :		List[str]				, _A  :		int				, _A  :		List[Any]={}      ):
											with torch.no_grad():
														UpperCAmelCase_      :       Any          =       model(**_A				, return_dict=_A				, **_A      )
														UpperCAmelCase_      :       List[Any]          =       model(**_A				, return_dict=_A				, **_A      ).to_tuple()

														def recursive_check(_A  :		List[Any]				, _A  :		Tuple      ):
																	if isinstance(_A				, (List, Tuple)      ):
																				for tuple_iterable_value, dict_iterable_value in zip(_A				, _A      ):
																							recursive_check(_A				, _A      )
																	elif isinstance(_A				, _A      ):
																				for tuple_iterable_value, dict_iterable_value in zip(
																				    tuple_object.values()				, dict_object.values()      ):
																							recursive_check(_A				, _A      )
																	elif tuple_object is None:
																				return
																	else:
																				self.assertTrue(
																				    torch.allclose(
																				        set_nan_tensor_to_zero(_A      )				, set_nan_tensor_to_zero(_A      )				, atol=1e-5      )				, msg=(
																				        '''Tuple and dict output are not equal. Difference:'''
																				        F" {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:"
																				        F" {torch.isnan(_A      ).any()} and `inf`: {torch.isinf(_A      )}. Dict has"
																				        F" `nan`: {torch.isnan(_A      ).any()} and `inf`: {torch.isinf(_A      )}."
																				    )				, )

														recursive_check(_A				, _A      )

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Tuple          =       model_class(_A      )
											model.to(_A      )
											model.eval()

											UpperCAmelCase_      :       Tuple          =       self._prepare_for_class(_A				, _A      )
											UpperCAmelCase_      :       Optional[Any]          =       self._prepare_for_class(_A				, _A      )
											check_equivalence(_A				, _A				, _A      )

											UpperCAmelCase_      :       str          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Optional[Any]          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											check_equivalence(_A				, _A				, _A      )

											UpperCAmelCase_      :       List[str]          =       self._prepare_for_class(_A				, _A      )
											UpperCAmelCase_      :       str          =       self._prepare_for_class(_A				, _A      )
											check_equivalence(_A				, _A				, _A				, {'''output_hidden_states''': True}      )

											UpperCAmelCase_      :       List[str]          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Tuple          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											check_equivalence(_A				, _A				, _A				, {'''output_hidden_states''': True}      )






@require_torch
class  snake_case__      (					unittest.TestCase     ,				UpperCamelCase):
					a_        =					(MaskFormerSwinBackbone,) if is_torch_available() else ()
					a_        =					MaskFormerSwinConfig




					def 		A      (  self  :		Optional[int]      )    ->							Any:
								UpperCAmelCase_      :       str          =       MaskFormerSwinModelTester(self      )





					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Union[str, Any]          =       inputs_dict['''pixel_values'''].shape[0]

								for backbone_class in self.all_model_classes:
											UpperCAmelCase_      :       List[str]          =       backbone_class(_A      )
											backbone.to(_A      )
											backbone.eval()

											UpperCAmelCase_      :       List[Any]          =       backbone(**_A      )

											# Test default outputs and verify feature maps
											self.assertIsInstance(outputs.feature_maps				, _A      )
											self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
											for feature_map, n_channels in zip(outputs.feature_maps				, backbone.channels      ):
														self.assertTrue(feature_map.shape[:2]				, (batch_size, n_channels)      )
											self.assertIsNone(outputs.hidden_states      )
											self.assertIsNone(outputs.attentions      )

											# Test output_hidden_states=True
											UpperCAmelCase_      :       Dict          =       backbone(**_A				, output_hidden_states=_A      )
											self.assertIsNotNone(outputs.hidden_states      )
											self.assertTrue(len(outputs.hidden_states      )				, len(backbone.stage_names      )      )
											# We skip the stem layer
											for hidden_states, n_channels in zip(outputs.hidden_states[1:]				, backbone.channels      ):
														for hidden_state in hidden_states:
																	# Hidden states are in the format (batch_size, (height * width), n_channels)
																	UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       hidden_state.shape
																	self.assertTrue((h_batch_size, h_n_channels)				, (batch_size, n_channels)      )

            # Test output_attentions=True
											if self.has_attentions:
														UpperCAmelCase_      :       Union[str, Any]          =       backbone(**_A				, output_attentions=_A      )
														self.assertIsNotNone(outputs.attentions      )










'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


